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# TrackR-CNN
Code for the TrackR-CNN baseline for the Multi Object Tracking and Segmentation (MOTS) task.
## Project website (including annotations)
## Paper
## mots_tools for evaluating results
## Running this code
### Setup
You'll need to install the following packages (possibly more):
tensorflow-gpu pycocotools numpy scipy sklearn pypng opencv-python munkres
In particular, the code has been tested with Python 3.6.7 and Tensorflow 1.13.1 running on a single GTX 1080 Ti gpu. While there is experimental support for multi-gpu training through the "gpus" config flag, some users have reported problems with this, so we recommend using one gpu only.
Furthermore, you'll need the [KITTI MOTS]( dataset, where we assume you have a folder /path/to/kitti_mots with subfolders /path/to/kitti_mots/images containing the input images (i.e. there exist subfolders /path/to/kitti_mots/images/0000, /path/to/kitti_mots/images/0001, ...) and /path/to/kitti_mots/instances (again with subfolders 0000, 0001, ...) containing the annotations.
Also, create the following directories for logs, model files etc. in the base directory of the repository:
mkdir forwarded models summaries logs
### Pre-Trained Models
Pre-trained models can be downloaded here:
### Folder structure and config flags
In the configuration files, you'll need to adjust the `KITTI_segtrack_data_dir` and `load_init` flags to point to the [KITTI MOTS]( data directory and the path to the [pretrained model](, respectively. Logs, checkpoints and summaries are stored in the `logs/`, `models/` and `summaries/` subdirectories.
So all in all, your folder structure should look like this:
-- train/
--- images/
---- 0000/
----- 000000.png
----- 000001.png
----- ...
---- 0001/
---- ...
--- instances/
---- 0000/
----- 000000.png
----- 000001.png
----- ...
---- 0001/
---- ...
- conv3d_sep2/
-- conv3d_sep2-00000005.index
-- conv3d_sep2-00000005.meta
- converted.meta
- converted.index
So point `KITTI_segtrack_data_dir` to `data/KITTI_MOTS/train/` and `load_init` to `models/converted`.
### Training
In order to train a model, run `` with the corresponding configuration file. For the baseline model with two separable 3D convolutions and data association with learned embeddings, use
python configs/conv3d_sep2
### Forwarding and tracking
Either first train your own model as described above, or download [our model]( and extract the files into models/conv3d_sep2/
To obtain the model's predictions (we call this "forwarding") run:
python configs/conv3d_sep2 "{\"task\":\"forward_tracking\",\"dataset\":\"KITTI_segtrack_feed\",\"load_epoch_no\":5,\"batch_size\":5,\"export_detections\":true,\"do_tracking\":false,\"video_tags_to_load\":[\"0002\",\"0006\",\"0007\",\"0008\",\"0010\",\"0013\",\"0014\",\"0016\",\"0018\",\"0000\",\"0001\",\"0003\",\"0004\",\"0005\",\"0009\",\"0011\",\"0012\",\"0015\",\"0017\",\"0019\",\"0020\"]}"
The json string supplied as an additional argument here overwrites the settings in the config file. Use `video_tags_to_load` to obtain predictions for specific sequences (in the example, all KITTI MOTS sequences are chosen). Output is written to the `forwarded/` subdirectory.
The model predictions as obtained by the previous command are not yet linked over time. You can use the following command to run the tracking algorithm described in the paper and to obtain final results in the `forwarded/` subdirectory which can be processed by the mots_tools scripts:
python configs/conv3d_sep2
You can also visualize the tracking results here by setting `visualize_tracks` to true.
### Tuning
The script for random tuning will find the best combination of tracking parameters on the training set and then evaluate these parameters on the validation set. This is how the results in the MOTS paper are obtained.
To use this script, run
python scripts/eval/ /path/to/detections/ /path/to/groundtruth/ /path/to/precomputed_optical_flow/ /path/to/output_file /path/to/tmp_folder/ /path/to/mots_eval/ association_type num_iterations
where `/path/to/detections/` is a folder containing the model output on the training set (obtained by the forwarding command above); `/path/to/mots_eval/` refers to the official evaluation script (link see above, clone the repository and supply the path here); `association_type` determines the method for associating detections into tracks and is either `reid` (using the association embeddings - use this if unsure!), `mask` (using mask warping), `bbox_iou` (using bounding box warping with median optical flow) or `bbox_center` (nearest neighbor matching); `num_iterations` is the number of random trials (1000 in the paper); `/path/to/groundtruth/ ` refers to the `instances` or `instances_txt` folder containing the annotations (which you can download from the project website); `/path/to/precomputed_optical_flow` can usually be set to a dummy folder (it refers to optical flow for input images, which is used only for a few of the ablation experiments in the paper - namely when setting `association_type` to `mask` or `bbox_iou` but if you set it to something else, then the flow path is ignored); at `/path/to/output_file`, a file will be created containing the results of the individual tuning iterations, please make sure this path is writable; at `/path/to/tmp_folder` a lot of intermediate folders will be stored, you can delete these afterwards.
## References
Parts of this code are based on Tensorpack ( and RETURNN (
## Citation
If you use this code, please cite:
author = {Paul Voigtlaender and Michael Krause and Aljo\u{s}a O\u{s}ep and Jonathon Luiten and Berin Balachandar Gnana Sekar and Andreas Geiger and Bastian Leibe},
title = {{MOTS}: Multi-Object Tracking and Segmentation},
booktitle = {CVPR},
year = {2019},
## License
MIT License
## Contact
If you find a problem in the code, please open an issue.
For general questions, please contact Paul Voigtlaender ( or Michael Krause (
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